Periodontitis and Outer Retinal Thickness: a Cross-Sectional Analysis of the United Kingdom Biobank Cohort

Purpose

Periodontitis, a ubiquitous severe gum disease affecting the teeth and surrounding alveolar bone, can heighten systemic inflammation. We investigated the association between very severe periodontitis and early biomarkers of age-related macular degeneration (AMD), in individuals with no eye disease.

Design

Cross-sectional analysis of the prospective community-based cohort United Kingdom (UK) Biobank.

Participants

Sixty-seven thousand three hundred eleven UK residents aged 40 to 70 years recruited between 2006 and 2010 underwent retinal imaging.

Methods

Macular-centered OCT images acquired at the baseline visit were segmented for retinal sublayer thicknesses. Very severe periodontitis was ascertained through a touchscreen questionnaire. Linear mixed effects regression modeled the association between very severe periodontitis and retinal sublayer thicknesses, adjusting for age, sex, ethnicity, socioeconomic status, alcohol consumption, smoking status, diabetes mellitus, hypertension, refractive error, and previous cataract surgery.

Main Outcome Measures

Photoreceptor layer (PRL) and retinal pigment epithelium-Bruch's membrane (RPE-BM) thicknesses.

Results

Among 36 897 participants included in the analysis, 1571 (4.3%) reported very severe periodontitis. Affected individuals were older, lived in areas of greater socioeconomic deprivation, and were more likely to be hypertensive, diabetic, and current smokers (all P < 0.001). On average, those with very severe periodontitis were hyperopic (0.05 ± 2.27 diopters) while those unaffected were myopic (-0.29 ± 2.40 diopters, P < 0.001). Following adjusted analysis, very severe periodontitis was associated with thinner PRL (-0.55 μm, 95% confidence interval [CI], -0.97 to -0.12; P = 0.022) but there was no difference in RPE-BM thickness (0.00 μm, 95% CI, -0.12 to 0.13; P = 0.97). The association between PRL thickness and very severe periodontitis was modified by age (P < 0.001). Stratifying individuals by age, thinner PRL was seen among those aged 60 to 69 years with disease (-1.19 μm, 95% CI, -1.85 to -0.53; P < 0.001) but not among those aged < 60 years.

Conclusions

Among those with no known eye disease, very severe periodontitis is statistically associated with a thinner PRL, consistent with incipient AMD. Optimizing oral hygiene may hold additional relevance for people at risk of degenerative retinal disease.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Treatment of age-related macular degeneration with aflibercept using a treat, extend and fixed protocol; A 4-year study of treatment outcomes, durability, safety and quality of life (An extension to the MATE randomised controlled trial)

PURPOSE

Data are limited pertaining to the long-term benefits of aflibercept treatment for neovascular age-related macular degeneration (nAMD). The aim of this study was to provide outcomes, safety, durability and quality-of-life data with aflibercept using a modified treat, extend and fixed regime over 4 years.

METHODS

Prospective, multicentre, single cohort observational study of treatment-naïve nAMD participants treated with aflibercept as 2-year extension of the MATE-trial that compared early and late Treat-and-Extend for 2 years. Refracted ETDRS best corrected visual acuity (BCVA), central retinal thickness (CRT), treatment interval and adverse events were assessed. Quality-of-life was measured using the Macular Disease Dependent Quality of Life (MacDQoL) and Macular Disease Treatment Satisfaction Questionnaires (MacTSQ).

RESULTS

Twenty-six of 40 participants completing the MATE-trial were enrolled with 20 completing the total 4-year study. Mean BCVA was 60.7 at Month 0 and 64.8 ETDRS letters at Month 48 while CRT decreased from 423.7 μm to 292.2 μm. Five participants discontinued treatment due to inactivity. The mean number of treatments and visits for the remaining participants was 27 and 30.0, respectively, with treatment intervals extended to 12 weeks in four participants at Month 48. Both AMD-specific QoL and treatment satisfaction remained stable between Months 0 and 48 and mean BCVA significantly correlated with AMD-specific QoL scores at Months 12, 24 and 48.

CONCLUSIONS

Results suggest that BCVA can be maintained over 48 months when following a treat-extend-and-fix regimen of aflibercept with intervals out to 12 weeks, while maintaining AMD-specific QoL and treatment satisfaction.

Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of More Than 3000 Inherited Retinal Disease Patients from the United Kingdom

Purpose

To quantify relevant fundus autofluorescence (FAF) image features cross-sectionally and longitudinally in a large cohort of inherited retinal diseases (IRDs) patients.

Design

Retrospective study of imaging data (55-degree blue-FAF on Heidelberg Spectralis) from patients.

Participants

Patients with a clinical and molecularly confirmed diagnosis of IRD who have undergone FAF 55-degree imaging at Moorfields Eye Hospital (MEH) and the Royal Liverpool Hospital (RLH) between 2004 and 2019.

Methods

Five FAF features of interest were defined: vessels, optic disc, perimacular ring of increased signal (ring), relative hypo-autofluorescence (hypo-AF) and hyper-autofluorescence (hyper-AF). Features were manually annotated by six graders in a subset of patients based on a defined grading protocol to produce segmentation masks to train an AI model, AIRDetect, which was then applied to the entire imaging dataset.

Main Outcome Measures

Quantitative FAF imaging features including area in mm 2 and vessel metrics, were analysed cross-sectionally by gene and age, and longitudinally to determine rate of progression. AIRDetect feature segmentation and detection were validated with Dice score and precision/recall, respectively.

Results

A total of 45,749 FAF images from 3,606 IRD patients from MEH covering 170 genes were automatically segmented using AIRDetect. Model-grader Dice scores for disc, hypo-AF, hyper-AF, ring and vessels were respectively 0.86, 0.72, 0.69, 0.68 and 0.65. The five genes with the largest hypo-AF areas were CHM , ABCC6 , ABCA4 , RDH12 , and RPE65 , with mean per-patient areas of 41.5, 30.0, 21.9, 21.4, and 15.1 mm 2 . The five genes with the largest hyper-AF areas were BEST1 , CDH23 , RDH12 , MYO7A , and NR2E3 , with mean areas of 0.49, 0.45, 0.44, 0.39, and 0.34 mm 2 respectively. The five genes with largest ring areas were CDH23 , NR2E3 , CRX , EYS and MYO7A, with mean areas of 3.63, 3.32, 2.84, 2.39, and 2.16 mm 2 . Vessel density was found to be highest in EFEMP1 , BEST1 , TIMP3 , RS1 , and PRPH2 (10.6%, 10.3%, 9.8%, 9.7%, 8.9%) and was lower in Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis genes. Longitudinal analysis of decreasing ring area in four RP genes ( RPGR, USH2A, RHO, EYS ) found EYS to be the fastest progressor at -0.18 mm 2 /year.

Conclusions

We have conducted the first large-scale cross-sectional and longitudinal quantitative analysis of FAF features across a diverse range of IRDs using a novel AI approach.

Diagnostic decisions of specialist optometrists exposed to ambiguous deep-learning outputs

Artificial intelligence (AI) has great potential in ophthalmology. We investigated how ambiguous outputs from an AI diagnostic support system (AI-DSS) affected diagnostic responses from optometrists when assessing cases of suspected retinal disease. Thirty optometrists (15 more experienced, 15 less) assessed 30 clinical cases. For ten, participants saw an optical coherence tomography (OCT) scan, basic clinical information and retinal photography ('no AI'). For another ten, they were also given AI-generated OCT-based probabilistic diagnoses ('AI diagnosis'); and for ten, both AI-diagnosis and AI-generated OCT segmentations ('AI diagnosis + segmentation') were provided. Cases were matched across the three types of presentation and were selected to include 40% ambiguous and 20% incorrect AI outputs. Optometrist diagnostic agreement with the predefined reference standard was lowest for 'AI diagnosis + segmentation' (204/300, 68%) compared to 'AI diagnosis' (224/300, 75% p = 0.010), and 'no Al' (242/300, 81%, p =  < 0.001). Agreement with AI diagnosis consistent with the reference standard decreased (174/210 vs 199/210, p = 0.003), but participants trusted the AI more (p = 0.029) with segmentations. Practitioner experience did not affect diagnostic responses (p = 0.24). More experienced participants were more confident (p = 0.012) and trusted the AI less (p = 0.038). Our findings also highlight issues around reference standard definition.

Deep-learning automated quantification of longitudinal OCT scans demonstrates reduced RPE loss rate, preservation of intact macular area and predictive value of isolated photoreceptor degeneration in geographic atrophy patients receiving C3 inhibition treatment

OBJECTIVE

To evaluate the role of automated optical coherence tomography (OCT) segmentation, using a validated deep-learning model, for assessing the effect of C3 inhibition on the area of geographic atrophy (GA); the constituent features of GA on OCT (photoreceptor degeneration (PRD), retinal pigment epithelium (RPE) loss and hypertransmission); and the area of unaffected healthy macula.To identify OCT predictive biomarkers for GA growth.

METHODS

Post hoc analysis of the FILLY trial using a deep-learning model for spectral domain OCT (SD-OCT) autosegmentation. 246 patients were randomised 1:1:1 into pegcetacoplan monthly (PM), pegcetacoplan every other month (PEOM) and sham treatment (pooled) for 12 months of treatment and 6 months of therapy-free monitoring. Only participants with Heidelberg SD-OCT were included (n=197, single eye per participant).The primary efficacy endpoint was the square root transformed change in area of GA as complete RPE and outer retinal atrophy (cRORA) in each treatment arm at 12 months, with secondary endpoints including RPE loss, hypertransmission, PRD and intact macular area.

RESULTS

Eyes treated PM showed significantly slower mean change of cRORA progression at 12 and 18 months (0.151 and 0.277 mm, p=0.0039; 0.251 and 0.396 mm, p=0.039, respectively) and RPE loss (0.147 and 0.287 mm, p=0.0008; 0.242 and 0.410 mm, p=0.00809). PEOM showed significantly slower mean change of RPE loss compared with sham at 12 months (p=0.0313). Intact macular areas were preserved in PM compared with sham at 12 and 18 months (p=0.0095 and p=0.044). PRD in isolation and intact macula areas was predictive of reduced cRORA growth at 12 months (coefficient 0.0195, p=0.01 and 0.00752, p=0.02, respectively) CONCLUSION: The OCT evidence suggests that pegcetacoplan slows progression of cRORA overall and RPE loss specifically while protecting the remaining photoreceptors and slowing the progression of healthy retina to iRORA.

Determinants of non-attendance at face-to-face and telemedicine ophthalmic consultations

BACKGROUND/AIMS

Evaluation of telemedicine care models has highlighted its potential for exacerbating healthcare inequalities. This study seeks to identify and characterise factors associated with non-attendance across face-to-face and telemedicine outpatient appointments.

METHODS

A retrospective cohort study at a tertiary-level ophthalmic institution in the UK, between 1 January 2019 and 31 October 2021. Logistic regression modelled non-attendance against sociodemographic, clinical and operational exposure variables for all new patient registrations across five delivery modes: asynchronous, synchronous telephone, synchronous audiovisual and face to face prior to the pandemic and face to face during the pandemic.

RESULTS

A total of 85 924 patients (median age 55 years, 54.4% female) were newly registered. Non-attendance differed significantly by delivery mode: (9.0% face to face prepandemic, 10.5% face to face during the pandemic, 11.7% asynchronous and 7.8%, synchronous during pandemic). Male sex, greater levels of deprivation, a previously cancelled appointment and not self-reporting ethnicity were strongly associated with non-attendance across all delivery modes. Individuals identifying as black ethnicity had worse attendance in synchronous audiovisual clinics (adjusted OR 4.24, 95% CI 1.59 to 11.28) but not asynchronous. Those not self-reporting their ethnicity were from more deprived backgrounds, had worse broadband access and had significantly higher non-attendance across all modes (all p<0.001).

CONCLUSION

Persistent non-attendance among underserved populations attending telemedicine appointments highlights the challenge digital transformation faces for reducing healthcare inequalities. Implementation of new programmes should be accompanied by investigation into the differential health outcomes of vulnerable populations.

Sickle Eye Project: a cross-sectional, non-interventional study of the prevalence of visual impairment due to sickle cell retinopathy and maculopathy in the UK

INTRODUCTION

Sickle cell disease (SCD) is one of the most common genetic disorders in the UK, with over 15 000 people affected. Proliferative sickle cell retinopathy (SCR) is a well-described complication of SCD and can result in significant sight loss, although the prevalence in the UK is not currently known. There are currently no national screening guidelines for SCR, with wide variations in the management of the condition across the UK.

METHODS AND ANALYSIS

The Sickle Eye Project is an epidemiological, cross-sectional, non-interventional study to determine the prevalence of visual impairment due to SCR and/or maculopathy in the UK. Haematologists in at least 16 geographically dispersed hospitals in the UK linked to participating eye clinics will offer study participation to consecutive patients meeting the inclusion criteria attending the sickle cell clinic. The following study procedures will be performed: (a) best corrected visual acuity with habitual correction and pinhole, (b) dilated slit lamp biomicroscopy and funduscopy, (c) optical coherence tomography (OCT), (d) OCT angiography where available, (e) ultrawide fundus photography, (f) National Eye Institute Visual Function Questionnaire-25 and (g) acceptability of retinal screening questionnaire. The primary outcome is the proportion of people with SCD with visual impairment defined as logarithm of the minimum angle of resolution ≥0.3 in at least one eye. Secondary outcomes include the prevalence of each stage of SCR and presence of maculopathy by age and genotype; correlation of stage of SCR and maculopathy to severity of SCD; the impact of SCR and presence of maculopathy on vision-related quality of life; and the acceptability to patients of routine retinal imaging for SCR and maculopathy.

ETHICS AND DISSEMINATION

Ethical approval was obtained from the South Central-Oxford A Research Ethics Committee (REC 23/SC/0363). Findings will be reported through academic journals in ophthalmology and haematology.

Evaluating the Effects of C3 Inhibition on Geographic Atrophy Progression from Deep-Learning OCT Quantification: A Split-Person Study

INTRODUCTION

To evaluate the effect pegcetacoplan, a C3 and C3b inhibitor, on the rate of progression of geographic atrophy (GA) as assessed by spectral domain optical coherence tomography (SD-OCT) using a split-person study design and deep-learning quantification.

METHODS

A post hoc analysis of phase 2 FILLY trial data comparing study (treated monthly, treated every other month and sham-treated) and fellow (untreated) eyes in a split-person study design was performed. This analysis included 288 eyes from 144 patients with bilateral GA from the FILLY phase 2 trial (Clinical Trials identifier: NCT02503332). Only patients with bilateral GA and without evidence of choroidal neovascularisation in either eye were included. Patient study eyes were treated with sham injections or with pegcetacoplan monthly (PM) or every other month (PEOM) for 12 months. SD-OCT scans of study and fellow eyes taken at baseline and 12 months were used for the analysis. The main outcomes were the annual change in the area of retinal pigment epithelial and outer retinal atrophy (RORA), its constituent features (photoreceptor degeneration [PRD], retinal pigment epithelium [RPE] loss, hypertransmission) and intact macula as compared to the untreated fellow eye.

RESULTS

Annual GA growth was reduced in eyes treated with PM versus untreated fellow eyes for OCT features, including RORA (study eye 0.792 vs. fellow eye 1.13 mm2; P = 0.003), PRD (0.739 vs. 1.23 mm2; P = 0.015), RPE-loss (0.789 vs. 1.17 mm2; P = 0.007) and intact macula (- 0.735 vs. - 1.29 mm2; P = 0.011). Similar (but not statistically significant) trends were observed with the PEOM treatment or when GA was quantified with fundus autofluorescence (FAF). The sham treatment demonstrated no effect. Pearson correlation coefficients showed concordance in the enlargement rate of GA between the study and fellow eyes in the sham (R = 0.64) and PEOM (R = 0.68) groups, but not in the PM group (R = 0.21).

CONCLUSIONS

Pegcetacoplan-treated eyes demonstrated a reduction in spatial GA progression compared to their untreated counterparts. This effect was more evident on OCT than with FAF.

TRIAL REGISTRATION

Clinical Trials identifier: NCT02503332.

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.

The effectiveness of interventions for optometric referrals into the hospital eye service: A review

PURPOSE

Ophthalmic services are currently under considerable stress; in the UK, ophthalmology departments have the highest number of outpatient appointments of any department within the National Health Service. Recognising the need for intervention, several approaches have been trialled to tackle the high numbers of false-positive referrals initiated in primary care and seen face to face within the hospital eye service (HES). In this mixed-methods narrative synthesis, we explored interventions based on their clinical impact, cost and acceptability to determine whether they are clinically effective, safe and sustainable. A systematic literature search of PubMed, MEDLINE and CINAHL, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), was used to identify appropriate studies published between December 2001 and December 2022.

RECENT FINDINGS

A total of 55 studies were reviewed. Four main interventions were assessed, where two studies covered more than one type: training and guidelines (n = 8), referral filtering schemes (n = 32), asynchronous teleophthalmology (n = 13) and synchronous teleophthalmology (n = 5). All four approaches demonstrated effectiveness for reducing false-positive referrals to the HES. There was sufficient evidence for stakeholder acceptance and cost-effectiveness of referral filtering schemes; however, cost comparisons involved assumptions. Referral filtering and asynchronous teleophthalmology reported moderate levels of false-negative cases (2%-20%), defined as discharged patients requiring HES monitoring.

SUMMARY

The effectiveness of interventions varied depending on which outcome and stakeholder was considered. More studies are required to explore stakeholder opinions around all interventions. In order to maximise clinical safety, it may be appropriate to combine more than one approach, such as referral filtering schemes with virtual review of discharged patients to assess the rate of false-negative cases. The implementation of a successful intervention is more complex than a 'one-size-fits-all' approach and there is potential space for newer types of interventions, such as artificial intelligence clinical support systems within the referral pathway.

Retinal Optical Coherence Tomography Features Associated With Incident and Prevalent Parkinson Disease

BACKGROUND AND OBJECTIVES

Cadaveric studies have shown disease-related neurodegeneration and other morphological abnormalities in the retina of individuals with Parkinson disease (PD); however, it remains unclear whether this can be reliably detected with in vivo imaging. We investigated inner retinal anatomy, measured using optical coherence tomography (OCT), in prevalent PD and subsequently assessed the association of these markers with the development of PD using a prospective research cohort.

METHODS

This cross-sectional analysis used data from 2 studies. For the detection of retinal markers in prevalent PD, we used data from AlzEye, a retrospective cohort of 154,830 patients aged 40 years and older attending secondary care ophthalmic hospitals in London, United Kingdom, between 2008 and 2018. For the evaluation of retinal markers in incident PD, we used data from UK Biobank, a prospective population-based cohort where 67,311 volunteers aged 40-69 years were recruited between 2006 and 2010 and underwent retinal imaging. Macular retinal nerve fiber layer (mRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layer (INL) thicknesses were extracted from fovea-centered OCT. Linear mixed-effects models were fitted to examine the association between prevalent PD and retinal thicknesses. Hazard ratios for the association between time to PD diagnosis and retinal thicknesses were estimated using frailty models.

RESULTS

Within the AlzEye cohort, there were 700 individuals with prevalent PD and 105,770 controls (mean age 65.5 ± 13.5 years, 51.7% female). Individuals with prevalent PD had thinner GCIPL (-2.12 μm, 95% CI -3.17 to -1.07, p = 8.2 × 10-5) and INL (-0.99 μm, 95% CI -1.52 to -0.47, p = 2.1 × 10-4). The UK Biobank included 50,405 participants (mean age 56.1 ± 8.2 years, 54.7% female), of whom 53 developed PD at a mean of 2,653 ± 851 days. Thinner GCIPL (hazard ratio [HR] 0.62 per SD increase, 95% CI 0.46-0.84, p = 0.002) and thinner INL (HR 0.70, 95% CI 0.51-0.96, p = 0.026) were also associated with incident PD.

DISCUSSION

Individuals with PD have reduced thickness of the INL and GCIPL of the retina. Involvement of these layers several years before clinical presentation highlight a potential role for retinal imaging for at-risk stratification of PD.

Home vision monitoring in patients with maculopathy: current and future options for digital technologies

Patients with macular pathology, including that caused by age-related macular degeneration and diabetic macular oedema, must attend frequent in-clinic monitoring appointments to detect onset of disease activity requiring treatment and to monitor progression of existing disease. In-person clinical monitoring places a significant burden on patients, caregivers and healthcare systems and is limited in that it only provides clinicians with a snapshot of the patient's disease status. The advent of remote monitoring technologies offers the potential for patients to test their own retinal health at home in collaboration with clinicians, reducing the need for in-clinic appointments. In this review we discuss visual function tests, both existing and novel, that have the potential for remote use and consider their suitability for discriminating the presence of disease and progression of disease. We then review the clinical evidence supporting the use of mobile applications for monitoring of visual function from clinical development through to validation studies and real-world implementation. This review identified seven app-based visual function tests: four that have already received some form of regulatory clearance and three under development. The evidence included in this review shows that remote monitoring offers great potential for patients with macular pathology to monitor their condition from home, reducing the need for burdensome clinic visits and expanding clinicians' understanding of patients' retinal health beyond traditional clinical monitoring. In order to instil confidence in the use of remote monitoring in both patients and clinicians further longitudinal real-world studies are now warranted.

Assessment of optometrists' referral accuracy and contributing factors: A review

PURPOSE

In the UK, ophthalmology has the highest number of outpatient appointments within the National Health Service. False-positive referrals from primary care are one of the main factors contributing to the oversubscription of hospital eye services (HESs). We reviewed the accuracy of referrals originating from primary care optometrists and contributing factors, such as condition type and years since registration.

RECENT FINDINGS

Of the 31 studies included in the review, 22 were retrospective analyses of referrals and appointments at the HES. Eight were prospective studies, and one used online clinical vignettes. Seven assessed the accuracy of referrals for all ocular conditions. The remaining studies focused on glaucoma (n = 11), cataracts (n = 7), emergency conditions (n = 4), neovascular age-related macular degeneration (n = 1) and paediatric binocular vision (n = 1). The diagnostic agreement for suspected emergency ocular conditions was the lowest, with only 21.1% of referrals considered to require urgent attention in one study. For glaucoma, the first-visit discharge rate was high (16.7%-48%). Optometrist referral accuracy was overall 18.6% higher than General Medical Practitioners'; however, the two mainly referred different ocular conditions. Female optometrists made more false-positive referrals than males (p = 0.008). The proportion of false positives decreased by 6.2% per year since registration (p < 0.001).

SUMMARY

There was significant variation in referral accuracy across different ocular conditions, partly due to differences when defining accurate referrals. Optometrists working in primary care are generally more limited in their resources than the HES. Thus, choosing the cautious option of referral when they are unsure could be in the patients' best interests. The possible effect of increased use of advanced imaging on referrals requires evaluation. Although interventions such as refinement schemes have been put in place, these vary across regions, and their approaches such as virtual referral triaging may reduce unnecessary HES face-to-face appointments and promote communication between primary and secondary care.

UK Biobank retinal imaging grading: methodology, baseline characteristics and findings for common ocular diseases

BACKGROUND/OBJECTIVES

This study aims to describe the grading methods and baseline characteristics for UK Biobank (UKBB) participants who underwent retinal imaging in 2009-2010, and to characterise individuals with retinal features suggestive of age-related macular degeneration (AMD), glaucoma and retinopathy.

METHODS

Non-mydriatic colour fundus photographs and macular optical coherence tomography (OCT) scans were manually graded by Central Administrative Research Facility certified graders and quality assured by clinicians of the Network of Ophthalmic Reading Centres UK. Captured retinal features included those associated with AMD (≥1 drusen, pigmentary changes, geographic atrophy or exudative AMD; either imaging modality), glaucoma (≥0.7 cup-disc ratio, ≥0.2 cup-disc ratio difference between eyes, other abnormal disc features; photographs only) and retinopathy (characteristic features of diabetic retinopathy with or without microaneurysms; either imaging modality). Suspected cases of these conditions were characterised with reference to diagnostic records, physical and biochemical measurements.

RESULTS

Among 68,514 UKBB participants who underwent retinal imaging, the mean age was 57.3 years (standard deviation 8.2), 45.7% were men and 90.6% were of White ethnicity. A total of 64,367 participants had gradable colour fundus photographs and 68,281 had gradable OCT scans in at least one eye. Retinal features suggestive of AMD and glaucoma were identified in 15,176 and 2184 participants, of whom 125 (0.8%) and 188 (8.6%), respectively, had a recorded diagnosis. Of 264 participants identified to have retinopathy with microaneurysms, 251 (95.1%) had either diabetes or hypertension.

CONCLUSIONS

This dataset represents a valuable addition to what is currently available in UKBB, providing important insights to both ocular and systemic health.

Visual and Anatomical Outcomes of a Single Intravitreal Dexamethasone in Diabetic Macular Edema: An 8 Year Real-World Study

IMPORTANCE

Diabetic macular edema (DME) is a major cause of vision loss in patients with diabetes mellitus. Intravitreal dexamethasone is a treatment option for patients unsuitable for or non-responsive to anti-angiogenic agents.

OBJECTIVE

To quantify visual and anatomical outcomes from an initial intravitreal dexamethasone injection over the expected 6-month period of dexamethasone release by the implant. Design and enrolment: This is a retrospective cohort study using electronic medical records of patients reviewed between 1 January 2012 and 1 April 2022.

SETTING

A tertiary eye-care center in London, United Kingdom; Moorfields Eye Hospital National Healthcare System Foundation Trust.

PARTICIPANTS

The cohort comprised 418 adult patients with DME who received an initial treatment of 700 µg intravitreal dexamethasone in the study period. Of these, 240 patients met the inclusion criteria of ≥2 hospital visits following initial injection (≥1 beyond 6 months) and no previous ocular corticosteroid treatment or missing assessment at baseline.

EXPOSURE(S)

Intravitreal dexamethasone implant (700 µg).

MAIN OUTCOME(S) AND MEASURE(S)

Probability of a positive visual outcome, defined as ≥5 or ≥10 Early Treatment Diabetic Retinopathy Study (ETDRS)-letter gain after treatment when compared to baseline (Kaplan-Meier models).

RESULTS

From the initial intravitreal dexamethasone injection alone, we observed a >75% chance of gaining ≥5 ETDRS letters and >50% chance of gaining ≥10 ETDRS letters within 6 months. There was less than a 50% chance of sustaining either positive visual outcome beyond 4 months.

CONCLUSIONS AND RELEVANCE

Most patients can be expected to have a positive visual outcome following an initial injection of dexamethasone implants that subsides within 4 months. Real-world re-treatment was observed to be delayed until after visual benefits were lost in half of the cohort. Further research will be needed to study the effects of delays in re-treatment.

SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease

Purpose

Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs).

Design

Diagnostic study of gene-labeled fundus autofluorescence (FAF) IRD images using deep learning.

Participants

Moorfields Eye Hospital (MEH) dataset of 15 692 FAF images obtained from 1800 patients with confirmed genetic diagnosis of 1 of 36 IRD genes.

Methods

A StyleGAN2 model is trained on the IRD dataset to generate 512 × 512 resolution images. Convolutional neural networks are trained for classification using different synthetically augmented datasets, including real IRD images plus 1800 and 3600 synthetic images, and a fully rebalanced dataset. We also perform an experiment with only synthetic data. All models are compared against a baseline convolutional neural network trained only on real data.

Main Outcome Measures

We evaluated synthetic data quality using a Visual Turing Test conducted with 4 ophthalmologists from MEH. Synthetic and real images were compared using feature space visualization, similarity analysis to detect memorized images, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score for no-reference-based quality evaluation. Convolutional neural network diagnostic performance was determined on a held-out test set using the area under the receiver operating characteristic curve (AUROC) and Cohen's Kappa (κ).

Results

An average true recognition rate of 63% and fake recognition rate of 47% was obtained from the Visual Turing Test. Thus, a considerable proportion of the synthetic images were classified as real by clinical experts. Similarity analysis showed that the synthetic images were not copies of the real images, indicating that copied real images, meaning the GAN was able to generalize. However, BRISQUE score analysis indicated that synthetic images were of significantly lower quality overall than real images (P < 0.05). Comparing the rebalanced model (RB) with the baseline (R), no significant change in the average AUROC and κ was found (R-AUROC = 0.86[0.85-88], RB-AUROC = 0.88[0.86-0.89], R-k = 0.51[0.49-0.53], and RB-k = 0.52[0.50-0.54]). The synthetic data trained model (S) achieved similar performance as the baseline (S-AUROC = 0.86[0.85-87], S-k = 0.48[0.46-0.50]).

Conclusions

Synthetic generation of realistic IRD FAF images is feasible. Synthetic data augmentation does not deliver improvements in classification performance. However, synthetic data alone deliver a similar performance as real data, and hence may be useful as a proxy to real data. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references.

Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study

BACKGROUND

Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed through interval screening by paediatric ophthalmologists. However, improved survival of premature neonates coupled with a scarcity of available experts has raised concerns about the sustainability of this approach. We aimed to develop bespoke and code-free deep learning-based classifiers for plus disease, a hallmark of ROP, in an ethnically diverse population in London, UK, and externally validate them in ethnically, geographically, and socioeconomically diverse populations in four countries and three continents. Code-free deep learning is not reliant on the availability of expertly trained data scientists, thus being of particular potential benefit for low resource health-care settings.

METHODS

This retrospective cohort study used retinal images from 1370 neonates admitted to a neonatal unit at Homerton University Hospital NHS Foundation Trust, London, UK, between 2008 and 2018. Images were acquired using a Retcam Version 2 device (Natus Medical, Pleasanton, CA, USA) on all babies who were either born at less than 32 weeks gestational age or had a birthweight of less than 1501 g. Each images was graded by two junior ophthalmologists with disagreements adjudicated by a senior paediatric ophthalmologist. Bespoke and code-free deep learning models (CFDL) were developed for the discrimination of healthy, pre-plus disease, and plus disease. Performance was assessed internally on 200 images with the majority vote of three senior paediatric ophthalmologists as the reference standard. External validation was on 338 retinal images from four separate datasets from the USA, Brazil, and Egypt with images derived from Retcam and the 3nethra neo device (Forus Health, Bengaluru, India).

FINDINGS

Of the 7414 retinal images in the original dataset, 6141 images were used in the final development dataset. For the discrimination of healthy versus pre-plus or plus disease, the bespoke model had an area under the curve (AUC) of 0·986 (95% CI 0·973-0·996) and the CFDL model had an AUC of 0·989 (0·979-0·997) on the internal test set. Both models generalised well to external validation test sets acquired using the Retcam for discriminating healthy from pre-plus or plus disease (bespoke range was 0·975-1·000 and CFDL range was 0·969-0·995). The CFDL model was inferior to the bespoke model on discriminating pre-plus disease from healthy or plus disease in the USA dataset (CFDL 0·808 [95% CI 0·671-0·909, bespoke 0·942 [0·892-0·982]], p=0·0070). Performance also reduced when tested on the 3nethra neo imaging device (CFDL 0·865 [0·742-0·965] and bespoke 0·891 [0·783-0·977]).

INTERPRETATION

Both bespoke and CFDL models conferred similar performance to senior paediatric ophthalmologists for discriminating healthy retinal images from ones with features of pre-plus or plus disease; however, CFDL models might generalise less well when considering minority classes. Care should be taken when testing on data acquired using alternative imaging devices from that used for the development dataset. Our study justifies further validation of plus disease classifiers in ROP screening and supports a potential role for code-free approaches to help prevent blindness in vulnerable neonates.

FUNDING

National Institute for Health Research Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and the University College London Institute of Ophthalmology.

TRANSLATIONS

For the Portuguese and Arabic translations of the abstract see Supplementary Materials section.

Association Between Retinal Features From Multimodal Imaging and Schizophrenia

Importance

The potential association of schizophrenia with distinct retinal changes is of clinical interest but has been challenging to investigate because of a lack of sufficiently large and detailed cohorts.

Objective

To investigate the association between retinal biomarkers from multimodal imaging (oculomics) and schizophrenia in a large real-world population.

Design, Setting, and Participants

This cross-sectional analysis used data from a retrospective cohort of 154 830 patients 40 years and older from the AlzEye study, which linked ophthalmic data with hospital admission data across England. Patients attended Moorfields Eye Hospital, a secondary care ophthalmic hospital with a principal central site, 4 district hubs, and 5 satellite clinics in and around London, United Kingdom, and had retinal imaging during the study period (January 2008 and April 2018). Data were analyzed from January 2022 to July 2022.

Main Outcomes and Measures

Retinovascular and optic nerve indices were computed from color fundus photography. Macular retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (mGC-IPL) thicknesses were extracted from optical coherence tomography. Linear mixed-effects models were used to examine the association between schizophrenia and retinal biomarkers.

Results

A total of 485 individuals (747 eyes) with schizophrenia (mean [SD] age, 64.9 years [12.2]; 258 [53.2%] female) and 100 931 individuals (165 400 eyes) without schizophrenia (mean age, 65.9 years [13.7]; 53 253 [52.8%] female) were included after images underwent quality control and potentially confounding conditions were excluded. Individuals with schizophrenia were more likely to have hypertension (407 [83.9%] vs 49 971 [48.0%]) and diabetes (364 [75.1%] vs 28 762 [27.6%]). The schizophrenia group had thinner mGC-IPL (-4.05 μm, 95% CI, -5.40 to -2.69; P = 5.4 × 10-9), which persisted when investigating only patients without diabetes (-3.99 μm; 95% CI, -6.67 to -1.30; P = .004) or just those 55 years and younger (-2.90 μm; 95% CI, -5.55 to -0.24; P = .03). On adjusted analysis, retinal fractal dimension among vascular variables was reduced in individuals with schizophrenia (-0.14 units; 95% CI, -0.22 to -0.05; P = .001), although this was not present when excluding patients with diabetes.

Conclusions and Relevance

In this study, patients with schizophrenia had measurable differences in neural and vascular integrity of the retina. Differences in retinal vasculature were mostly secondary to the higher prevalence of diabetes and hypertension in patients with schizophrenia. The role of retinal features as adjunct outcomes in patients with schizophrenia warrants further investigation.

The MATE trial: a multicentre, mixed-methodology, pilot, randomised controlled trial in neovascular age-related macular degeneration

BACKGROUND/OBJECTIVES

In healthcare research investigating complex interventions, gaps in understanding of processes can be filled by using qualitative methods alongside a quantitative approach. The aim of this mixed-methods pilot trial was to provide feasibility evidence comparing two treatment regimens for neovascular age-related macular degeneration (nAMD) to inform a future large-scale randomised controlled trial (RCT).

SUBJECTS/METHODS

Forty-four treatment-naïve nAMD patients were followed over 24 months and randomised to one of two treatment regimens: standard care (SC) or treat and extend (T&E). The primary objective evaluated feasibility of the MATE trial via evaluations of screening logs for recruitment rates, nonparticipation and screen fails, whilst qualitative in-depth interviews with key study staff evaluated the recruitment phase and running of the trial. The secondary objective assessed changes in visual acuity and central retinal thickness (CRT) between the two treatment arms.

RESULTS

The overall recruitment rate was 3.07 participants per month with a 40.8% non-participation rate, 18.51% screen-failure rate and 15% withdrawal/non-completion rate. Key themes in the recruitment phase included human factors, protocol-related issues, recruitment processes and challenges. Both treatment regimens showed a trend towards a visual acuity gain at month 12 which was not maintained at month 24, whilst CRT reduced similarly in both regimens over the same time period. These were achieved with one less treatment following a T&E regimen.

CONCLUSION

This mixed-methodology, pilot RCT achieved its pre-defined recruitment, nonparticipation and screen failure rates, thus deeming it a success. With some minor protocol amendments, progression to a large-scale RCT will be achievable.

Can artificial intelligence accelerate the diagnosis of inherited retinal diseases? Protocol for a data-only retrospective cohort study (Eye2Gene)

INTRODUCTION

Inherited retinal diseases (IRD) are a leading cause of visual impairment and blindness in the working age population. Mutations in over 300 genes have been found to be associated with IRDs and identifying the affected gene in patients by molecular genetic testing is the first step towards effective care and patient management. However, genetic diagnosis is currently slow, expensive and not widely accessible. The aim of the current project is to address the evidence gap in IRD diagnosis with an AI algorithm, Eye2Gene, to accelerate and democratise the IRD diagnosis service.

METHODS AND ANALYSIS

The data-only retrospective cohort study involves a target sample size of 10 000 participants, which has been derived based on the number of participants with IRD at three leading UK eye hospitals: Moorfields Eye Hospital (MEH), Oxford University Hospital (OUH) and Liverpool University Hospital (LUH), as well as a Japanese hospital, the Tokyo Medical Centre (TMC). Eye2Gene aims to predict causative genes from retinal images of patients with a diagnosis of IRD. For this purpose, 36 most common causative IRD genes have been selected to develop a training dataset for the software to have enough examples for training and validation for detection of each gene. The Eye2Gene algorithm is composed of multiple deep convolutional neural networks, which will be trained on MEH IRD datasets, and externally validated on OUH, LUH and TMC.

ETHICS AND DISSEMINATION

This research was approved by the IRB and the UK Health Research Authority (Research Ethics Committee reference 22/WA/0049) 'Eye2Gene: accelerating the diagnosis of IRDs' Integrated Research Application System (IRAS) project ID: 242050. All research adhered to the tenets of the Declaration of Helsinki. Findings will be reported in an open-access format.

Biomarkers of macular neovascularisation activity using optical coherence tomography angiography in treated stable neovascular age related macular degeneration

BACKGROUND

The aim of this study was to describe features of disease activity in patients with treated stable macular neovascularisation (MNV) in neovascular age related macular degeneration (nAMD) using optical coherence tomography angiography (OCTA).

METHODS

Thirty-two eyes of 32 patients with nAMD were included in this prospective, observational study. These patients were undergoing treatment with aflibercept on a treat-and-extend regimen attending an extension to a 12-week treatment interval.

RESULTS

All subjects had no macular haemorrhage and no structural OCT markers of active MNV activity at the index 12-week treatment extension visit. 31/32 OCTA images were gradeable without significant imaging artefact. The mean MNV size was 3.6mm² ± 4.6mm² and 27 (87.1%) had detectable MNV blood flow. 29/31 (93.5%) subjects had MNV with mature phenotypes including 10 non-specific, 10 tangle and 3 deadtree phenotypes. MNV halo and MNV central feeder vessel were noted in 18 (58.1%) and 19 (61.3%) of subjects respectively; only 1 (3.2%) subject was noted to have a MNV capillary fringe.

CONCLUSIONS

MNV blood flow is still detectable using OCTA in the majority of subjects in this study with treated stable MNV. OCTA features associated included MNV mature phenotype, MNV feeder vessel, MNV halo and absence of capillary fringe.

Safety and efficacy of an artificial intelligence-enabled decision tool for treatment decisions in neovascular age-related macular degeneration and an exploration of clinical pathway integration and implementation: protocol for a multi-methods validation study

INTRODUCTION

Neovascular age-related macular degeneration (nAMD) management is one of the largest single-disease contributors to hospital outpatient appointments. Partial automation of nAMD treatment decisions could reduce demands on clinician time. Established artificial intelligence (AI)-enabled retinal imaging analysis tools, could be applied to this use-case, but are not yet validated for it. A primary qualitative investigation of stakeholder perceptions of such an AI-enabled decision tool is also absent. This multi-methods study aims to establish the safety and efficacy of an AI-enabled decision tool for nAMD treatment decisions and understand where on the clinical pathway it could sit and what factors are likely to influence its implementation.

METHODS AND ANALYSIS

Single-centre retrospective imaging and clinical data will be collected from nAMD clinic visits at a National Health Service (NHS) teaching hospital ophthalmology service, including judgements of nAMD disease stability or activity made in real-world consultant-led-care. Dataset size will be set by a power calculation using the first 127 randomly sampled eligible clinic visits. An AI-enabled retinal segmentation tool and a rule-based decision tree will independently analyse imaging data to report nAMD stability or activity for each of these clinic visits. Independently, an external reading centre will receive both clinical and imaging data to generate an enhanced reference standard for each clinic visit. The non-inferiority of the relative negative predictive value of AI-enabled reports on disease activity relative to consultant-led-care judgements will then be tested. In parallel, approximately 40 semi-structured interviews will be conducted with key nAMD service stakeholders, including patients. Transcripts will be coded using a theoretical framework and thematic analysis will follow.

ETHICS AND DISSEMINATION

NHS Research Ethics Committee and UK Health Research Authority approvals are in place (21/NW/0138). Informed consent is planned for interview participants only. Written and oral dissemination is planned to public, clinical, academic and commercial stakeholders.

Associations Between HbA1c Across the Normal Range, Diagnosed, and Undiagnosed Diabetes and Retinal Layer Thickness in UK Biobank Cohort

Purpose

The purpose of this study was to investigate the association between glycated hemoglobin (HbA1c) levels and retinal sub-layer thicknesses in people with and without diabetes.

Methods

We included 41,453 UK Biobank participants aged 40 to 69 years old. Diabetes status was defined by self-report of diagnosis or use of insulin. Participants were categorized into groups: (1) those with HbA1c <48 mmol/mol were subdivided into quintiles according to normal range of HbA1c; (2) those previously diagnosed with diabetes with no evidence of diabetic retinopathy; and (3) undiagnosed diabetes: >48 mmol/mol. Total macular and retinal sub-layer thicknesses were derived from spectral-domain optical coherence tomography (SD-OCT) images. Multivariable linear regression was used to evaluate the associations between diabetes status and retinal layer thickness.

Results

Compared with participants in the second quintile of the normal HbA1c range, those in the fifth quintile had a thinner photoreceptor layer thickness (-0.33 µm, P = 0.006). Participants with diagnosed diabetes had a thinner macular retinal nerve fiber layer (mRNFL; -0.58 µm, P < 0.001), photoreceptor layer thickness (-0.94 µm, P < 0.001), and total macular thickness (-1.61 µm, P < 0.001), whereas undiagnosed diabetes participants had a reduced photoreceptor layer thickness (-1.22 µm, P = 0.009) and total macular thickness (-2.26 µm, P = 0.005). Compared to participants without diabetes, those with diabetes had a thinner mRNFL (-0.50 µm, P < 0.001), photoreceptor layer thickness (-0.77 µm, P < 0.001), and total macular thickness (-1.36 µm, P < 0.001).

Conclusions

Participants with higher HbA1c in the normal range had marginally thinner photoreceptor thickness, whereas those with diabetes (including undiagnosed diabetes) had meaningfully thinner retinal sublayer and total macular thickness.

Translational Relevance

We showed that early retinal neurodegeneration occurs in people whose HbA1c levels are below the current diabetes diagnostic threshold; this might impact the management of pre-diabetes individuals.

Prediction of visual function from automatically quantified optical coherence tomography biomarkers in patients with geographic atrophy using machine learning

Geographic atrophy (GA) is a vision-threatening manifestation of age-related macular degeneration (AMD), one of the leading causes of blindness globally. Objective, rapid, reliable, and scalable quantification of GA from optical coherence tomography (OCT) retinal scans is necessary for disease monitoring, prognostic research, and clinical endpoints for therapy development. Such automatically quantified biomarkers on OCT are likely to further elucidate structure-function correlation in GA and thus the pathophysiological mechanisms of disease development and progression. In this work, we aimed to predict visual function with machine-learning applied to automatically acquired quantitative imaging biomarkers in GA. A post-hoc analysis of data from a clinical trial and routine clinical care was conducted. A deep-learning automated segmentation model was applied on OCT scans from 476 eyes (325 patients) with GA. A separate machine learning prediction model (Random Forest) used the resultant quantitative OCT (qOCT) biomarkers to predict cross-sectional visual acuity under standard (VA) and low luminance (LLVA). The primary outcome was regression coefficient (r2) and mean absolute error (MAE) for cross-sectional VA and LLVA in Early Treatment Diabetic Retinopathy Study (ETDRS) letters. OCT parameters were predictive of VA (r2 0.40 MAE 11.7 ETDRS letters) and LLVA (r2 0.25 MAE 12.1). Normalised random forest feature importance, as a measure of the predictive value of the three constituent features of GA; retinal pigment epithelium (RPE)-loss, photoreceptor degeneration (PDR), hypertransmission and their locations, was reported both on voxel-level heatmaps and ETDRS-grid subfields. The foveal region (46.5%) and RPE-loss (31.1%) had greatest predictive importance for VA. For LLVA, however, non-foveal regions (74.5%) and PDR (38.9%) were most important. In conclusion, automated qOCT biomarkers demonstrate predictive significance for VA and LLVA in GA. LLVA is itself predictive of GA progression, implying that the predictive qOCT biomarkers provided by our model are also prognostic.

Visual acuity outcome of stable proliferative diabetic retinopathy following initial complete panretinal photocoagulation

Background

Recent clinical trials on proliferative diabetic retinopathy (PDR) show that presenting visual acuity can be stabilised with panretinal photocoagulation (PRP) within 2 years despite the need for supplementary PRP or anti-vascular endothelial growth factor therapy for concomitant diabetic macular oedema (DMO). It is unclear whether similar results can be obtained in daily clinical practice. Here, we query the probability of vision loss in patients with treatment-naïve PDR who have attained stability after PRP and its predictors.

Methods

Retrospective cohort study at a tertiary eye centre between 01 January 2015 and 31 December 2019, wherein 2336 eyes met study criteria with first record of stable PRP-treated PDR in at least one eye. Kaplan-Meier and Cox proportional hazards modelling were used to report the probability of vision loss of at least five Early Treatment Diabetic Retinopathy Study (ETDRS) letters.

Results

The probability of losing at least five ETDRS letters was 50% at 3.32 (95% CI, 2.94 to 3.78) years after achieving first stability post PRP in treatment-naïve PDR. The mean decrease at this event was 14.2 (SD 13.0) ETDRS letters irrespective of the presence of DMO. The strongest risk factor for vision loss was a history of DMO at baseline (HR 1.62 (95% CI, 1.34 to 1.95), p<0.001).

Discussion

One in two patients with stable treated PDR lose a line of vision by 3.5 years. This resulted in 15% of patients losing their eligibility to drive. Notably, 13% of the cohort died during the follow-up period.

Long-term Retinal Morphology and Functional Associations in Treated Neovascular Age-Related Macular Degeneration: Findings from the Inhibition of VEGF in Age-Related Choroidal Neovascularisation Trial

PURPOSE

To describe the frequency of long-term morphologic features and their relationships with visual function in participants who exited the Inhibition of VEGF in Age-Related Choroidal Neovascularisation (IVAN; ISRCTN92166560) trial.

DESIGN

Multicenter cohort study up to 7 years after enrollment.

PARTICIPANTS

Patients enrolled in the IVAN trial, excluding participants who died or withdrew during the trial.

METHODS

Multimodal fundus images, best-corrected visual acuity (BCVA), and low-luminance visual acuity (LLVA) were obtained for a subset of 199 participants who attended a research visit. Clinical sites (n = 20) also provided all visual acuity and clinical information from usual care records for 532 participants and submitted the most recent color, OCT, and other fundus images for 468 participants to a reading center.

MAIN OUTCOME MEASURES

Assessed the following from the most recent images: intralesional macular atrophy (ILMA) within the footprint of the neovascular lesion; hyperreflective material (HRM); intraretinal fluid (IRF); subretinal fluid (SRF); pigment epithelial detachment (PED); and disorganized retinal outer layers (DROLs). Cross-sectional relationships between morphologic features and BCVA/LLVA were estimated.

RESULTS

Intralesional macular atrophy was present in 31.8% of the study eyes at IVAN exit (mean follow-up, 1.96 years) and 89.5% at the most recent imaging visit (mean follow-up, 6.18 years). Hyperreflective material, IRF, SRF, PED, and DROLs were present in 78.8%, 47.7%, 7.6%, 94.5%, and 55% of the study eyes, respectively. In the subset with complete imaging data, in eyes without DROL, the BCVA was worst in the thinnest outer fovea tertile (thinnest minus middle and thickest tertiles, -19.7 and -19.5 letters, respectively), whereas in eyes with DROL, the BCVA was worst in the thickest (thinnest and middle tertiles minus thickest, 12.5 and 12.2, respectively). Regression models showed that the presence of ILMA and HRM was independently associated with BCVA (22 letters worse [95% confidence interval {CI}, -11.2 to -32.8; P < 0.001] and 9.8 letters worse [95% CI, -0.1 to -19.4; P = 0.047], respectively). Subretinal fluid and foveal PED were associated with better BCVA (5.9 letters [95% CI, -7.9 to 19.7; P = 0.399] and 6.4 letters [95% CI, -1.1 to 14.0; P = 0.094], respectively). The model with LLVA was similar. A sensitivity analysis involving the entire eligible cohort yielded similar estimates.

CONCLUSIONS

Macular atrophy and HRM were common after 7 years of follow-up and strongly associated with visual outcomes.

Protocol for a qualitative study to explore acceptability, barriers and facilitators of the implementation of new teleophthalmology technologies between community optometry practices and hospital eye services

INTRODUCTION

Novel teleophthalmology technologies have the potential to reduce unnecessary and inaccurate referrals between community optometry practices and hospital eye services and as a result improve patients' access to appropriate and timely eye care. However, little is known about the acceptability and facilitators and barriers to the implementations of these technologies in real life.

METHODS AND ANALYSIS

A theoretically informed, qualitative study will explore patients' and healthcare professionals' perspectives on teleophthalmology and Artificial Intelligence Decision Support System models of care. A combination of situated observations in community optometry practices and hospital eye services, semistructured qualitative interviews with patients and healthcare professionals and self-audiorecordings of healthcare professionals will be conducted. Participants will be purposively selected from 4 to 5 hospital eye services and 6-8 affiliated community optometry practices. The aim will be to recruit 30-36 patients and 30 healthcare professionals from hospital eye services and community optometry practices. All interviews will be audiorecorded, with participants' permission, and transcribed verbatim. Data from interviews, observations and self-audiorecordings will be analysed thematically and will be informed by normalisation process theory and an inductive approach.

ETHICS AND DISSEMINATION

Ethical approval has been received from London-Bromley research ethics committee. Findings will be reported through academic journals and conferences in ophthalmology, health services research, management studies and human-computer interaction.

Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data

PURPOSE

To generate a prognostic model to predict keratoconus progression to corneal crosslinking (CXL).

DESIGN

Retrospective cohort study.

METHODS

We recruited 5025 patients (9341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients were available. We investigated both keratometry or CXL as end points for progression and used the Royston-Parmar method on the proportional hazards scale to generate a prognostic model. We calculated hazard ratios (HRs) for each significant covariate, with explained variation and discrimination, and performed internal-external cross validation by geographic regions.

RESULTS

After exclusions, model fitting comprised 8701 eyes, of which 3232 underwent CXL. For early keratoconus, CXL provided a more robust prognostic model than keratometric progression. The final model explained 33% of the variation in time to event: age HR (95% CI) 0.9 (0.90-0.91), maximum anterior keratometry 1.08 (1.07-1.09), and minimum corneal thickness 0.95 (0.93-0.96) as significant covariates. Single-nucleotide polymorphisms (SNPs) associated with keratoconus (n=28) did not significantly contribute to the model. The predicted time-to-event curves closely followed the observed curves during internal-external validation. Differences in discrimination between geographic regions was low, suggesting the model maintained its predictive ability.

CONCLUSIONS

A prognostic model to predict keratoconus progression could aid patient empowerment, triage, and service provision. Age at presentation is the most significant predictor of progression risk. Candidate SNPs associated with keratoconus do not contribute to progression risk.

Artificial Intelligence and Imaging Processing in Optical Coherence Tomography and Digital Images in Uveitis

INTRODUCTION

Computer vision, understood as the area of science that trains computers to interpret digital images through both artificial intelligence (AI) and classical algorithms, has significantly advanced the analysis and interpretation of optical coherence tomography (OCT) in retina research. The aim of this review is to summarise the recent advances of computer vision in imaging processing in uveitis, with a particular focus in optical coherence tomography images.

MATERIAL AND METHODS

Literature review.

RESULTS

The development of computer vision to assist uveitis diagnosis and prognosis is still undergoing, but important efforts have been made in the field.

CONCLUSION

The automatising of image processing in uveitis could be fundamental to establish objective and standardised outcomes for future clinical trials. In addition, it could help to better understand the disease and its progression.

AlzEye: longitudinal record-level linkage of ophthalmic imaging and hospital admissions of 353 157 patients in London, UK

PURPOSE

Retinal signatures of systemic disease ('oculomics') are increasingly being revealed through a combination of high-resolution ophthalmic imaging and sophisticated modelling strategies. Progress is currently limited not mainly by technical issues, but by the lack of large labelled datasets, a sine qua non for deep learning. Such data are derived from prospective epidemiological studies, in which retinal imaging is typically unimodal, cross-sectional, of modest number and relates to cohorts, which are not enriched with subpopulations of interest, such as those with systemic disease. We thus linked longitudinal multimodal retinal imaging from routinely collected National Health Service (NHS) data with systemic disease data from hospital admissions using a privacy-by-design third-party linkage approach.

PARTICIPANTS

Between 1 January 2008 and 1 April 2018, 353 157 participants aged 40 years or older, who attended Moorfields Eye Hospital NHS Foundation Trust, a tertiary ophthalmic institution incorporating a principal central site, four district hubs and five satellite clinics in and around London, UK serving a catchment population of approximately six million people.

FINDINGS TO DATE

Among the 353 157 individuals, 186 651 had a total of 1 337 711 Hospital Episode Statistics admitted patient care episodes. Systemic diagnoses recorded at these episodes include 12 022 patients with myocardial infarction, 11 735 with all-cause stroke and 13 363 with all-cause dementia. A total of 6 261 931 retinal images of seven different modalities and across three manufacturers were acquired from 1 54 830 patients. The majority of retinal images were retinal photographs (n=1 874 175) followed by optical coherence tomography (n=1 567 358).

FUTURE PLANS

AlzEye combines the world's largest single institution retinal imaging database with nationally collected systemic data to create an exceptional large-scale, enriched cohort that reflects the diversity of the population served. First analyses will address cardiovascular diseases and dementia, with a view to identifying hidden retinal signatures that may lead to earlier detection and risk management of these life-threatening conditions.

Teleophthalmology-enabled and artificial intelligence-ready referral pathway for community optometry referrals of retinal disease (HERMES): a Cluster Randomised Superiority Trial with a linked Diagnostic Accuracy Study-HERMES study report 1-study protocol

INTRODUCTION

Recent years have witnessed an upsurge of demand in eye care services in the UK. With a large proportion of patients referred to Hospital Eye Services (HES) for diagnostics and disease management, the referral process results in unnecessary referrals from erroneous diagnoses and delays in access to appropriate treatment. A potential solution is a teleophthalmology digital referral pathway linking community optometry and HES.

METHODS AND ANALYSIS

The HERMES study (Teleophthalmology-enabled and artificial intelligence-ready referral pathway for community optometry referrals of retinal disease: a cluster randomised superiority trial with a linked diagnostic accuracy study) is a cluster randomised clinical trial for evaluating the effectiveness of a teleophthalmology referral pathway between community optometry and HES for retinal diseases. Nested within HERMES is a diagnostic accuracy study, which assesses the accuracy of an artificial intelligence (AI) decision support system (DSS) for automated diagnosis and referral recommendation. A postimplementation, observational substudy, a within-trial economic evaluation and discrete choice experiment will assess the feasibility of implementation of both digital technologies within a real-life setting. Patients with a suspicion of retinal disease, undergoing eye examination and optical coherence tomography (OCT) scans, will be recruited across 24 optometry practices in the UK. Optometry practices will be randomised to standard care or teleophthalmology. The primary outcome is the proportion of false-positive referrals (unnecessary HES visits) in the current referral pathway compared with the teleophthalmology referral pathway. OCT scans will be interpreted by the AI DSS, which provides a diagnosis and referral decision and the primary outcome for the AI diagnostic study is diagnostic accuracy of the referral decision made by the Moorfields-DeepMind AI system. Secondary outcomes relate to inappropriate referral rate, cost-effectiveness analyses and human-computer interaction (HCI) analyses.

ETHICS AND DISSEMINATION

Ethical approval was obtained from the London-Bromley Research Ethics Committee (REC 20/LO/1299). Findings will be reported through academic journals in ophthalmology, health services research and HCI.

TRIAL REGISTRATION NUMBER

ISRCTN18106677 (protocol V.1.1).

Enablers and Barriers to Deployment of Smartphone-Based Home Vision Monitoring in Clinical Practice Settings

Question

What are the enablers and barriers of patient engagement for app-based home vision monitoring at scale?

Findings

In this cohort and survey study including 417 adults, 258 patients were active users (61.9%) of whom 166 patients (64.3%) were compliant users. Engagement was positively associated with higher comfort with technology, White British ethnicity, visual acuity, neovascular age-related macular degeneration diagnosis, and the number of intravitreal injections and was negatively associated with increased age.

Meaning

These findings suggest effective smartphone app-based home vision monitoring should address the risk factors for low engagement and digital exclusion during clinical practice setting deployment.

Importance

Telemedicine is accelerating the remote detection and monitoring of medical conditions, such as vision-threatening diseases. Meaningful deployment of smartphone apps for home vision monitoring should consider the barriers to patient uptake and engagement and address issues around digital exclusion in vulnerable patient populations.

Objective

To quantify the associations between patient characteristics and clinical measures with vision monitoring app uptake and engagement.

Design, Setting, and Participants

In this cohort and survey study, consecutive adult patients attending Moorfields Eye Hospital receiving intravitreal injections for retinal disease between May 2020 and February 2021 were included.

Exposures

Patients were offered the Home Vision Monitor (HVM) smartphone app to self-test their vision. A patient survey was conducted to capture their experience. App data, demographic characteristics, survey results, and clinical data from the electronic health record were analyzed via regression and machine learning.

Main Outcomes and Measures

Associations of patient uptake, compliance, and use rate measured in odds ratios (ORs).

Results

Of 417 included patients, 236 (56.6%) were female, and the mean (SD) age was 72.8 (12.8) years. A total of 258 patients (61.9%) were active users. Uptake was negatively associated with age (OR, 0.98; 95% CI, 0.97-0.998; P = .02) and positively associated with both visual acuity in the better-seeing eye (OR, 1.02; 95% CI, 1.00-1.03; P = .01) and baseline number of intravitreal injections (OR, 1.01; 95% CI, 1.00-1.02; P = .02). Of 258 active patients, 166 (64.3%) fulfilled the definition of compliance. Compliance was associated with patients diagnosed with neovascular age-related macular degeneration (OR, 1.94; 95% CI, 1.07-3.53; P = .002), White British ethnicity (OR, 1.69; 95% CI, 0.96-3.01; P = .02), and visual acuity in the better-seeing eye at baseline (OR, 1.02; 95% CI, 1.01-1.04; P = .04). Use rate was higher with increasing levels of comfort with use of modern technologies (β = 0.031; 95% CI, 0.007-0.055; P = .02). A total of 119 patients (98.4%) found the app either easy or very easy to use, while 96 (82.1%) experienced increased reassurance from using the app.

Conclusions and Relevance

This evaluation of home vision monitoring for patients with common vision-threatening disease within a clinical practice setting revealed demographic, clinical, and patient-related factors associated with patient uptake and engagement. These insights inform targeted interventions to address risks of digital exclusion with smartphone-based medical devices.

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review

BACKGROUND

Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage, corneal collagen cross-linking can prevent disease progression and further visual loss. Although advanced forms are easily detected, reliable identification of subclinical disease can be problematic. Several different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of multiple types of clinical measures, such as corneal imaging, aberrometry, or biomechanical measurements.

OBJECTIVE

The aim of this study is to survey and critically evaluate the literature on the algorithmic detection of subclinical keratoconus and equivalent definitions.

METHODS

For this systematic review, we performed a structured search of the following databases: MEDLINE, Embase, and Web of Science and Cochrane Library from January 1, 2010, to October 31, 2020. We included all full-text studies that have used algorithms for the detection of subclinical keratoconus and excluded studies that did not perform validation. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations.

RESULTS

We compared the measured parameters and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison, including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm, and key results are reported in this study.

CONCLUSIONS

Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Currently, there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early treatment to prevent disease progression.

Agreement Between Spectral-Domain and Swept-Source Optical Coherence Tomography Retinal Thickness Measurements in Macular and Retinal Disease

INTRODUCTION

To assess inter-device agreement in optical coherence tomography-derived retinal thickness measurements in patients with known macular conditions between spectral-domain and swept-source optical coherence tomography (OCT).

METHODS

Two hundred seventy-two subjects were included in the study. They consisted of 91 male (33.5%) and 181 female (66.5%) subjects, and 132 left (48.5%) and 140 right (51.5%) eyes. Each subject underwent spectral-domain OCT (SD-OCT, Spectralis, Heidelberg Engineering; RTVue XR Avanti XR HD, Optovue) and swept-source OCT (SS-OCT; DRI-OCT-1, Atlantis, Topcon) in a single imaging session performed by the same clinical trial-certified technician. The comparison of retinal thickness reproducibility between devices was performed using Bland-Altman analyses and across the entire data set using the intraclass correlation coefficient (ICC).

RESULTS

The ICC of the retinal thickness measurements (95% confidence interval) made using all three OCT instruments was 0.81 (0.77-0.84). The mean difference in mean retinal thickness between Spectralis SD-OCT and Topcon SS-OCT was 59.1 μm (95% limit of agreement [LoA] -21.7 to 139.8 μm). The mean difference in mean retinal thickness between Optovue SD-OCT and Topcon SS-OCT was 21.8 μm (95% LoA -34.7  to 78.3 μm).

CONCLUSIONS

Retinal layer thickness measurements vary between SS-OCT and SD-OCT devices. We describe inter-device agreement in retinal thickness between SS-OCT and SD-OCT in patients with macular conditions. Clinicians should be aware of the differences in retinal thickness values when imaging patients using different OCT devices and should consider using the same OCT device model in order to monitor clinical change.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier (NCT02828215).

Dynamic volume-rendered optical coherence tomography pupillometry

PURPOSE

To assess intrapupillary space (IPS) changes in healthy subjects with regard to decreased iris motility in patients with pseudoexfoliation glaucoma (PEXG) or non-arteritic anterior ischaemic optic neuropathy (NAION) in a feasibility study in a clinical environment.

METHODS

Scotopic and photopic IPS measurements using three-dimensionally rendered swept-source optical coherence tomography (SS-OCT) data were obtained and compared for all subjects. Intrapupillary space (IPS) parameters were evaluated such as absolute volumetric differences, relative light response for volumetric ratios and pupillary ejection fraction (PEF) for functional contraction measurements.

RESULTS

From a total of 122 IPS from 66 subjects, 106 IPS were eligible for comparison providing values for 72 normal, 30 PEXG and 4 NAION eyes. In healthy, PEXG and NAION subjects, scotopic overall mean IPS was 8.90, 3.45 and 4.16 mm³ , and photopic overall mean IPS was 0.87, 0.74 and 1.13 mm³ , respectively. Three-dimensional contractility showed a mean absolute difference of 8.03 mm³ for normals (defined as 100% contractility), 2.72 mm³ for PEXG (33.88% of normal) and 3.03 mm³ for NAION (38.50% of normal) with a relative light response ratio between scotopic and photopic volumes of 10.26 (100%), 4.69 (45.70%) and 3.67 (35.78%), respectively. Pupillary ejection fraction (PEF) showed a contractile pupillary emptying of 88.11% for normals, 76.92% for PEXG and 70.91% for NAION patients.

CONCLUSION

This 3D pupillometry OCT assessment allows for quantitative measurements of pupil function, contractility and response to light. More specifically, PEF is presented as a potential (neuro)-pupillary outcome measure that could be useful in the monitoring of ophthalmic disorders that affect pupillary function.

Uncovering of intraspecies macular heterogeneity in cynomolgus monkeys using hybrid machine learning optical coherence tomography image segmentation

The fovea is a depression in the center of the macula and is the site of the highest visual acuity. Optical coherence tomography (OCT) has contributed considerably in elucidating the pathologic changes in the fovea and is now being considered as an accompanying imaging method in drug development, such as antivascular endothelial growth factor and its safety profiling. Because animal numbers are limited in preclinical studies and automatized image evaluation tools have not yet been routinely employed, essential reference data describing the morphologic variations in macular thickness in laboratory cynomolgus monkeys are sparse to nonexistent. A hybrid machine learning algorithm was applied for automated OCT image processing and measurements of central retina thickness and surface area values. Morphological variations and the effects of sex and geographical origin were determined. Based on our findings, the fovea parameters are specific to the geographic origin. Despite morphological similarities among cynomolgus monkeys, considerable variations in the foveolar contour, even within the same species but from different geographic origins, were found. The results of the reference database show that not only the entire retinal thickness, but also the macular subfields, should be considered when designing preclinical studies and in the interpretation of foveal data.

Phenotyping of retinal neovascularization in ischemic retinal vein occlusion using wide field OCT angiography

BACKGROUND/OBJECTIVES

Abnormal retinal neovascularization caused by ischemic retinal vein occlusion (RVO) is a frequent cause of visually significant vitreous hemorrhage. The early detection of new vessels may be challenging and often requires the use of invasive tests such as fundus fluorescein angiography (FA). We demonstrate the use of wide-field optical coherence tomography angiography (WF-OCTA) in the detection and characterization of neovascularization secondary to ischemic RVO.

SUBJECTS/METHODS

We conducted a retrospective observational case series of patients diagnosed with ischemic RVO between August 2018 and March 2019, who underwent WF-SS-OCTA imaging (PLEX Elite 9000, Carl Zeiss Meditec). We performed real-life montage imaging, covering the involved area and compared the findings of WF-SS-OCTA to standard clinical examination and when available, ultrawide-field fluorescein angiography (UWF-FA, Optos 200TX).

RESULTS

In the included 39 eyes with ischemic RVO, neovascularization elsewhere (NVE) was encountered in 16 of 39 eyes (41%) on WF-OCTA and were characterized as sea-fan type vessels and nodular type vessels, based on their appearance and localization. NVE was identified in 4/39 eyes on standard clinical examination, equating to a detection rate of 10.3%. All were of a sea-fan morphology. In one case, NVE found on WF-OCTA was not observed on UWF-FA, which was a nodular type. Neovascularization of the disc (NVD) was detected in one eye.

CONCLUSIONS

WF-OCTA may become a useful noninvasive tool in the detection of neovascularization in patients with ischemic RVO. Furthermore, the characterization of different morphologies of neovascularization detected by WF-OCTA could be of clinical relevance.

Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study

BACKGROUND

Geographic atrophy is a major vision-threatening manifestation of age-related macular degeneration, one of the leading causes of blindness globally. Geographic atrophy has no proven treatment or method for easy detection. Rapid, reliable, and objective detection and quantification of geographic atrophy from optical coherence tomography (OCT) retinal scans is necessary for disease monitoring, prognostic research, and to serve as clinical endpoints for therapy development. To this end, we aimed to develop and validate a fully automated method to detect and quantify geographic atrophy from OCT.

METHODS

We did a deep-learning model development and external validation study on OCT retinal scans at Moorfields Eye Hospital Reading Centre and Clinical AI Hub (London, UK). A modified U-Net architecture was used to develop four distinct deep-learning models for segmentation of geographic atrophy and its constituent retinal features from OCT scans acquired with Heidelberg Spectralis. A manually segmented clinical dataset for model development comprised 5049 B-scans from 984 OCT volumes selected randomly from 399 eyes of 200 patients with geographic atrophy secondary to age-related macular degeneration, enrolled in a prospective, multicentre, phase 2 clinical trial for the treatment of geographic atrophy (FILLY study). Performance was externally validated on an independently recruited dataset from patients receiving routine care at Moorfields Eye Hospital (London, UK). The primary outcome was segmentation and classification agreement between deep-learning model geographic atrophy prediction and consensus of two independent expert graders on the external validation dataset.

FINDINGS

The external validation cohort included 884 B-scans from 192 OCT volumes taken from 192 eyes of 110 patients as part of real-life clinical care at Moorfields Eye Hospital between Jan 1, 2016, and Dec, 31, 2019 (mean age 78·3 years [SD 11·1], 58 [53%] women). The resultant geographic atrophy deep-learning model produced predictions similar to consensus human specialist grading on the external validation dataset (median Dice similarity coefficient [DSC] 0·96 [IQR 0·10]; intraclass correlation coefficient [ICC] 0·93) and outperformed agreement between human graders (DSC 0·80 [0·28]; ICC 0·79). Similarly, the three independent feature-specific deep-learning models could accurately segment each of the three constituent features of geographic atrophy: retinal pigment epithelium loss (median DSC 0·95 [IQR 0·15]), overlying photoreceptor degeneration (0·96 [0·12]), and hypertransmission (0·97 [0·07]) in the external validation dataset versus consensus grading.

INTERPRETATION

We present a fully developed and validated deep-learning composite model for segmentation of geographic atrophy and its subtypes that achieves performance at a similar level to manual specialist assessment. Fully automated analysis of retinal OCT from routine clinical practice could provide a promising horizon for diagnosis and prognosis in both research and real-life patient care, following further clinical validation FUNDING: Apellis Pharmaceuticals.

Fundus Autofluorescence Imaging in Macular Telangiectasia Type 2: MacTel Study Report Number 9

PURPOSE

To investigate the role of fundus autofluorescence (FAF) imaging in the diagnosis of macular telangiectasia type 2 (MacTel) and to describe disease-associated FAF patterns and their origin.

DESIGN

Cross-sectional multicenter study METHODS: FAF images were collected from the multicenter MacTel Natural History Observation and Registry Study. In a first qualitative approach, common FAF phenotypes were defined and correlated with multimodal imaging. We then evaluated how many eyes showed FAF changes, and temporal vs nasal asymmetry of FAF changes was graded. Finally, 100 eyes of MacTel patients and 100 control eyes (50 normal eyes and 50 eyes with other macular diseases) were combined and 2 masked graders assessed the presence of MacTel based on FAF images alone.

RESULTS

The study included 807 eyes of 420 patients (33 eyes were excluded owing to poor image quality). Loss of macular pigment, cystoid spaces, pigment plaques, neovascular membranes, and ectatic vascular changes commonly caused characteristic changes on FAF images. All MacTel patients had macular FAF changes in at least 1 eye. In 95% of eyes, these changes were more pronounced temporally than nasally. Common FAF patterns were increased (60%) and mixed/decreased FAF (38%) and/or visibility of vascular changes such as blunted vessels or ectatic capillaries (79%). Based on those features, high diagnostic performance was achieved for detection of the disease based on FAF alone (Youden index up to 0.91).

CONCLUSIONS

The study demonstrates that MacTel is consistently associated with disease-specific changes on FAF imaging. Those changes are typically more pronounced in the temporal parafovea.

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning

Question

Is deep learning–based segmentation of macular disease in optical coherence tomography (OCT) suitable for clinical use?

Findings

In this diagnostic study of OCT data from 173 patients with age-related macular degeneration or diabetic macular edema, model segmentations qualitatively ranked better or comparable for clinical applicability to 1 or more expert grader segmentations in 127 scans (73%) by a panel of 3 retinal specialists. Scans with high quantitative accuracy scores were not reliably associated with higher rankings.

Meaning

These findings suggest that qualitative evaluation adds to quantitative approaches when assessing clinical applicability of segmentation tools and clinician satisfaction in practice.

Importance

Quantitative volumetric measures of retinal disease in optical coherence tomography (OCT) scans are infeasible to perform owing to the time required for manual grading. Expert-level deep learning systems for automatic OCT segmentation have recently been developed. However, the potential clinical applicability of these systems is largely unknown.

Objective

To evaluate a deep learning model for whole-volume segmentation of 4 clinically important pathological features and assess clinical applicability.

Design, Setting, Participants

This diagnostic study used OCT data from 173 patients with a total of 15 558 B-scans, treated at Moorfields Eye Hospital. The data set included 2 common OCT devices and 2 macular conditions: wet age-related macular degeneration (107 scans) and diabetic macular edema (66 scans), covering the full range of severity, and from 3 points during treatment. Two expert graders performed pixel-level segmentations of intraretinal fluid, subretinal fluid, subretinal hyperreflective material, and pigment epithelial detachment, including all B-scans in each OCT volume, taking as long as 50 hours per scan. Quantitative evaluation of whole-volume model segmentations was performed. Qualitative evaluation of clinical applicability by 3 retinal experts was also conducted. Data were collected from June 1, 2012, to January 31, 2017, for set 1 and from January 1 to December 31, 2017, for set 2; graded between November 2018 and January 2020; and analyzed from February 2020 to November 2020.

Main Outcomes and Measures

Rating and stack ranking for clinical applicability by retinal specialists, model-grader agreement for voxelwise segmentations, and total volume evaluated using Dice similarity coefficients, Bland-Altman plots, and intraclass correlation coefficients.

Results

Among the 173 patients included in the analysis (92 [53%] women), qualitative assessment found that automated whole-volume segmentation ranked better than or comparable to at least 1 expert grader in 127 scans (73%; 95% CI, 66%-79%). A neutral or positive rating was given to 135 model segmentations (78%; 95% CI, 71%-84%) and 309 expert gradings (2 per scan) (89%; 95% CI, 86%-92%). The model was rated neutrally or positively in 86% to 92% of diabetic macular edema scans and 53% to 87% of age-related macular degeneration scans. Intraclass correlations ranged from 0.33 (95% CI, 0.08-0.96) to 0.96 (95% CI, 0.90-0.99). Dice similarity coefficients ranged from 0.43 (95% CI, 0.29-0.66) to 0.78 (95% CI, 0.57-0.85).

Conclusions and Relevance

This deep learning–based segmentation tool provided clinically useful measures of retinal disease that would otherwise be infeasible to obtain. Qualitative evaluation was additionally important to reveal clinical applicability for both care management and research.

Smartphone-based remote monitoring of vision in macular disease enables early detection of worsening pathology and need for intravitreal therapy

Background/aims

To assess the outcomes of home monitoring of distortion caused by macular diseases using a smartphone-based application (app), and to examine them with hospital-based assessments of visual acuity (VA), optical coherence tomography-derived central macular thickness (CMT) and the requirement of intravitreal injection therapy.

Design

Observational study with retrospective analysis of data.

Methods

Participants were trained in the correct use of the app (Alleye, Oculocare, Zurich, Switzerland) in person or by using video and telephone consultations. Automated threshold-based alerts were communicated based on a traffic light system. A ‘threshold alarm’ was defined as three consecutive ‘red’ scores, and turned into a ‘persistent alarm’ if present for greater than a 7-day period. Changes of VA and CMT, and the requirement for intravitreal therapy after an alarm were examined.

Results

245 patients performing a total of 11 592 tests (mean 46.9 tests per user) were included and 85 eyes (164 alarms) examined. Mean drop in VA from baseline was −4.23 letters (95% CI: −6.24 to −2.22; p<0.001) and mean increase in CMT was 29.5 µm (95% CI: −0.08 to 59.13; p=0.051). Sixty-six eyes (78.5%) producing alarms either had a drop in VA, increase in CMT or both and 60.0% received an injection. Eyes with persistent alarms had a greater loss of VA, −4.79 letters (95% CI: −6.73 to −2.85; p<0.001) or greater increase in CMT, +87.8 µm (95% CI: 5.2 to 170.4; p=0.038).

Conclusion

Smartphone-based self-tests for macular disease may serve as reliable indicators for the worsening of pathology and the need for treatment.

Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning

PURPOSE

We sought to develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD).

DESIGN

Development and validation of a deep-learning model for feature segmentation.

METHODS

Data for model development were obtained from 307 optical coherence tomography volumes. Eight experienced graders manually delineated all abnormalities in 2712 B-scans. A deep neural network was trained with these data to perform voxel-level segmentation of the 13 most common abnormalities (features). For evaluation, 112 B-scans from 112 patients with a diagnosis of neovascular AMD were annotated by 4 independent observers. The main outcome measures were Dice score, intraclass correlation coefficient, and free-response receiver operating characteristic curve.

RESULTS

On 11 of 13 features, the model obtained a mean Dice score of 0.63 ± 0.15, compared with 0.61 ± 0.17 for the observers. The mean intraclass correlation coefficient for the model was 0.66 ± 0.22, compared with 0.62 ± 0.21 for the observers. Two features were not evaluated quantitatively because of a lack of data. Free-response receiver operating characteristic analysis demonstrated that the model scored similar or higher sensitivity per false positives compared with the observers.

CONCLUSIONS

The quality of the automatic segmentation matches that of experienced graders for most features, exceeding human performance for some features. The quantified parameters provided by the model can be used in the current clinical routine and open possibilities for further research into treatment response outside clinical trials.

1

PURPOSE

Describe the development, delivery, acceptability and evaluation of a modular training programme for community-based, non-medical practitioners monitoring patients with quiescent neovascular age related macular degeneration (QnAMD). Also, report on a qualitative process evaluation conducted during the pilot phase of a randomised control trial (the FENETRE Study) exploring patient and practitioner acceptability of community-based QnAMD care relative to hospital-based care.

METHODS

Learning outcomes from The College of Optometrists' Medical Retina higher qualifications and the Royal College of Ophthalmologists' Common Clinical Competency Framework were used to develop a competency framework for QnAMD care. Training was delivered online, comprising six asynchronous lectures followed by two synchronous case-based discussion webinars, with an accredited assessment of 24 case vignettes. An anonymous evaluation survey was conducted with the first two FENETRE cohorts (n = 38). Separately, we undertook a qualitative process evaluation, sampling purposively in four hospitals and five community-based practices, interviewing nine patients and eight practitioners.

RESULTS

Survey responses (n = 26) showed community optometrists were very satisfied (n = 12; 46%) or satisfied (n = 14; 54%) with the training; feedback reflected by qualitative process evaluation data. Overall, optometrists also felt either confident (n = 15; 58%) or very confident (n = 8; 31%) in conducting AMD monitoring appointments following training, a finding also corroborated by interview data from optometrists participating in the initial pilot phase roll-out. Optometrists identified patient convenience and alleviating pressures in hospital care as the primary reasons for acceptability of community pathways. Data from patients entering community practices suggested they largely found this at least as safe and convenient as hospital care, although some patients randomised to hospital care perceived that as safer.

CONCLUSION

This pilot study has shown the development and implementation of a collaborative community monitoring model is feasible, with satisfaction from community optometrists for training and accreditation, and broad acceptance for the pathway by both patients and practitioners.

FENETRE study: quality-assured follow-up of quiescent neovascular age-related macular degeneration by non-medical practitioners: study protocol and statistical analysis plan for a randomised controlled trial

OBJECTIVE

Management of age-related macular degeneration (AMD) places a high demand on already constrained hospital-based eye services. This study aims to assess the safety and quality of follow-up within the community led by suitably trained non-medical practitioners for the management of quiescent neovascular AMD (QnAMD).

METHODS/DESIGN

This is a prospective, multisite, randomised clinical trial. 742 participants with QnAMD will be recruited and randomised to either continue hospital-based secondary care or to receive follow-up within a community setting. Participants in both groups will be monitored for disease reactivation over the course of 12 months and referred for treatment as necessary. Outcomes measures will assess the non-inferiority of primary care follow-up accounting for accuracy of the identification of disease reactivation, patient loss to follow-up and accrued costs and the budget impact to the National Health Service.

ETHICS AND DISSEMINATION

Research ethics approval was obtained from the London Bloomsbury Ethics Committee. The results of this study will be disseminated through academic peer-reviewed publications, conferences and collaborations with eye charities to insure the findings reach the appropriate patient populations.

TRIAL REGISTRATION NUMBER

NCT03893474.